Philippe Dollfus

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—Memristive nanodevices can feature a compact multi-level non-volatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing(More)
This work proposes two learning architectures based on memristive nanodevices. First, we present an unsupervised architecture that is capable of discerning characteristic features in unlabeled inputs. The memristive nanodevices are used as synapses and learn thanks to simple voltage pulses which implement a simplified "Spike Timing Dependent Plasticity"(More)
This work discusses the modeling of memristive devices, for architectures where they are used as synapses. It is shown that the most common models used in this context do not always accurately reflect the actual behavior of popular devices in pulse regime. We introduce a new behavioral model, intended towards the nanoarchitecture community. It fits the(More)
We present a quantum transport simulation of graphene field-effect transistors based on the self consistent solution of 2D-Poisson solver and Dirac equation within the non-equilibrium Green's function formalism. The device operation of double gate 2D-graphene field effect transistors is investigated. The study emphasizes the band-to-band and Klein tunneling(More)
—This paper discusses the influence of the channel impurity distribution on the transport and the drive current in short-gate MOSFETs. A careful description of electron–ion interaction suitable for the case of discrete impurities has been implemented in a three-dimensional particle Monte Carlo simu-lator. This transport model is applied to the investigation(More)
Direct tunneling gate currents of ultrathin gate oxide thickness metal oxide semiconductor field effect transistors ͑MOSFETs͒ are modeled in a two-step calculation procedure based on the treatment of physical microscopic data acquired during Monte Carlo device simulation. Gate currents are obtained by weighting the carrier perpendicular energy distribution(More)
This paper presents the results of a comparison among five Monte Carlo device simu-lators for nano-scale MOSFETs. These models are applied to the simulation of the I-V characteristics of a 25 nm gate-length MOSFET representative of the high-performance transistor of the 65 nm technology node. Appreciable differences between the simu-lators are obtained in(More)
Different kind of devices: → in strong connection with industrial R&D:-"conventional" transistors (MOSFET, HEMT, …): towards nm scale → more advanced devices for nanoelectronics:-quantum dots and single electron devices-quantum wires, carbon nanotubes and related devices-resonant tunelling diode) Different approaches to transport modelling:-Semi-classical(More)
This article presents a study of the giant piezoresistance effect in p-type silicon using full-band Monte Carlo simulation. This effect has been demonstrated experimentally in Si nanowires by He and Yang [1]. By introducing a law of variation of the surface potential according to the applied mechanical stress, we can reproduce this effect. The modulation of(More)